Here is an example blog post.
#> [[ 0 1 2 3]
#> [ 4 5 6 7]
#> [ 8 9 10 11]
#> [12 13 14 15]
#> [16 17 18 19]
#> [20 21 22 23]
#> [24 25 26 27]
#> [28 29 30 31]
#> [32 33 34 35]
#> [36 37 38 39]
#> [40 41 42 43]]
We can also sneakily print python objects in R
And grab R objects in python
#> V1 V2 V3 V4
#> 0 0.0 2.0 4.0 6.0
#> 1 8.0 10.0 12.0 14.0
#> 2 16.0 18.0 20.0 22.0
#> 3 24.0 26.0 28.0 30.0
#> 4 32.0 34.0 36.0 38.0
#> 5 40.0 42.0 44.0 46.0
#> 6 48.0 50.0 52.0 54.0
#> 7 56.0 58.0 60.0 62.0
#> 8 64.0 66.0 68.0 70.0
#> 9 72.0 74.0 76.0 78.0
#> 10 80.0 82.0 84.0 86.0
And we can also import between languages
sklearn = reticulate::import("sklearn")
rf = sklearn$ensemble$RandomForestRegressor(n_estimators = 100)
rf#> RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
#> max_features='auto', max_leaf_nodes=None,
#> min_impurity_decrease=0.0, min_impurity_split=None,
#> min_samples_leaf=1, min_samples_split=2,
#> min_weight_fraction_leaf=0.0, n_estimators=100.0,
#> n_jobs=None, oob_score=False, random_state=None,
#> verbose=0, warm_start=False)